I am building an ANN model to predict customer churn.
X is a list and y is a single array. I am getting the following error on model.fit():
Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>*
Code:
import tensorflow as tf
from tensorflow import keras
model = keras.Sequential([
keras.layers.Dense(26, input_shape=(26,), activation='relu'),
keras.layers.Dense(15, activation='relu'),
keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(X_train, y_train, epochs=100)
Error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [68], in <cell line: 16>()
10 opt = keras.optimizers.Adam(learning_rate=0.01)
12 model.compile(optimizer='adam',
13 loss='binary_crossentropy',
14 metrics=['accuracy'])
---> 16 model.fit(X_train, y_train, epochs=100)
File ~\.conda\envs\tensorfl\lib\site-packages\keras\utils\traceback_utils.py:67, in filter_traceback.<locals>.error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
File ~\.conda\envs\tensorfl\lib\site-packages\keras\engine\data_adapter.py:985, in select_data_adapter(x, y)
982 adapter_cls = [cls for cls in ALL_ADAPTER_CLS if cls.can_handle(x, y)]
983 if not adapter_cls:
984 # TODO(scottzhu): This should be a less implementation-specific error.
--> 985 raise ValueError(
986 "Failed to find data adapter that can handle "
987 "input: {}, {}".format(
988 _type_name(x), _type_name(y)))
989 elif len(adapter_cls) > 1:
990 raise RuntimeError(
991 "Data adapters should be mutually exclusive for "
992 "handling inputs. Found multiple adapters {} to handle "
993 "input: {}, {}".format(
994 adapter_cls, _type_name(x), _type_name(y)))
ValueError: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'pandas.core.frame.DataFrame'>